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According to the current Intergovernmental Panel of Climate Change report, climate change will increase the probability of occurrence of droughts in some areas. Recent contributions at the international level indicate that trade is expected to act as an efficient tool to mitigate the adverse effect of future climate conditions on agriculture. However, no contribution has focused on the similar capacity of trade within any country yet. The U.S. is an obvious choice given that many climate impact studies focus on its agriculture and around 90% of the U.S. crop trade is domestic. Combining a recent state‐to‐state trade flow dataset with detailed drought records at a fine spatial and temporal resolution, this paper highlights first that trade increases as the destination state experiences more drought and inversely in the origin state. As a result, crop growers' profits depend on both local and trade partners' weather conditions. Projections based on future weather data convert the crop grower's expected loss without trade into expected profit. As such, this paper challenges the estimates of the current climate impact literature and concludes that trade is expected to act as a $14.5 billion mitigation tool in the near future.

Objective To evaluate the association of weather, seasons, months and holidays on the frequency and pattern of pediatric facial fractures. Methods Retrospective review of pediatric patients treated for facial fractures at two Level I trauma centers in a midsize Midwestern US city over a 5‐year period. Patients were included only if presentation was within 3 hours of inciting trauma, transfers from other facilities were excluded. Demographic characteristics, fracture patterns, operative interventions, weather data, and local public school schedules were acquired and associations were analyzed with unpaired t tests, χ2, multivariate and binomial regression model analyses. Results Two hundred and sixty patients were included. The average age (SD) was 11.8 (5.0) years, with 173 males and 87 females. The highest distribution of presentations occurred in the summer season (35.0%), on weekends and holidays (58.1%), and when the weather was described as clear (48.5%). The most common mechanisms of injury were motor vehicle collisions (25.8%), followed by sports—(21.5%) and assault—(16.5%) related injuries. Mechanisms were significantly associated with certain fracture patterns. Older age was associated with fewer orbital fractures (P, A retrospective review of 260 pediatric facial trauma patients found an association between the incidence of facial fractures and warmer weather, clear skies, weekends or holidays, and mechanism of injury. Older age is associated with operative intervention. These results can be used to guide public health interventions, policymaking, and facial trauma resource allocation.

We describe the digitization of 1.8 million sub‐daily and daily weather observations which were recorded in the UK Met Office Daily Weather Reports during the 1900–1910 period. The data were rescued from scanned images of the original documents by 2,148 volunteer citizen scientists using the weatherrescue.org website. The rescued observations include dry and wet bulb temperatures, daily maximum and minimum temperatures, daily rainfall amounts and sub‐daily sea‐level pressure from 72 different locations across western Europe. These observations will be used to fill gaps in existing pressure, temperature and rainfall records and are one of the largest recoveries of weather data by citizen scientists. The value of these additional observations is highlighted by comparing the pressure observations to the Twentieth Century Reanalysis version 3 ensemble for some specific case studies.

PM2.5 mass concentration prediction is an important research issue because of the increasing impact of air pollution on the urban environment. In this paper, a PM2.5 forecasting framework incorporating meteorological factors based on multiple kernel learning (MKL) is proposed to forecast the near future PM2.5. In addition, we develop a novel two‐step algorithm for solving the primal MKL problem. Compared with most existing MKL 2‐step algorithms, the proposed algorithm does not require the optimal step size for updating kernel combination coefficients by linear search. To demonstrate the performance of the proposed forecasting framework, its performance is compared to single kernel‐based support vector regression (SVR). Data sets of an inland city Beijing acquired from UCI are used to train and validate both of two methods. Experiments show that our proposed method outperforms the SVR.

Background Cold weather is reportedly a precipitator of reversible cerebral vasoconstriction syndrome (RCVS) in a few cases. We systematically investigated whether meteorological factors correlate with the occurrence of RCVS. Methods We conducted a retrospective analysis of a cohort of patients diagnosed with RCVS or probable RCVS, based on International Classification of Headache Disorders, third edition (ICHD-3) criteria, in a hospital-based headache center from March 2005 to February 2014. Monthly averages of local weather data measured in Taipei were obtained from the Central Weather Bureau in Taiwan. Primary weather variables were compared with the number of monthly new-onset cases of RCVS. Results We recruited 226 patients with established RCVS and 72 patients with probable RCVS during a 108-month study period. Incidence of RCVS was higher in winter than summer months (3.3 persons/month [SD: 2.0] vs 2.1 persons/month [SD: 1.5], P = .013). The monthly incidence of RCVS correlated negatively with mean daily temperature (r = -0.231, P = .016) and average precipitation (r = -0.269, P = .005), but positively with barometric pressure (r = 0.274, P = .004). These 3 correlated meteorological factors together explained about 10% of the variance in RCVS monthly incidence (R2 = 0.095, P = .015). Conclusion RCVS was found to be more common in winter months and to be associated with weather variables in Taiwan. Further studies are needed to explore the underlying mechanisms of these associations.

Regional, automated meteorological networks, such as the Oklahoma Mesonet can potentially provide high quality forcing data for generating gridded surfaces, but proven methods of interpolating weather variables between the station locations are needed. We compared two interpolation methods, ordinary kriging (OK) and empirical Bayesian kriging (EBK), with and without using long‐term climate imprints (CI), for creating spatially continuous, daily weather datasets. Daily meteorological variables (maximum and minimum temperature, solar radiation, and precipitation) from the Oklahoma Mesonet for the period 1997–2014 were interpolated using geoprocessing tools in ArcGIS. Cross‐validation was used for evaluation of interpolation methods, with 90% of sites chosen randomly for the training set and the remaining 10% left for validation. For all interpolation approaches, cross‐validation showed coefficient of determination (R2) values of .99 and .98 for daily maximum and minimum air temperatures, with mean absolute error (MAE) ranging from ±0.45–0.50 °C for maximum temperature and ±0.77–0.80 °C for minimum temperature. Likewise, for daily solar radiation, R2 values of .94 and .93 showed overall good prediction accuracy with MAE values 1.00 and 1.01 MJ m–2 d–1 for EBK and OK, respectively. However, for rainfall, all methods yielded R2 values ≤.67, suggesting a need for more effective interpolation method. Based on its lower computational time and lower input data requirement, OK appears preferable to the other approaches tested here to provide the daily weather data for gridded models in Oklahoma and other regions with similar monitoring networks.

Weather- and climate-related hazards are responsible for monetary losses, material damages, and societal consequences. Quantifying related risks is, therefore, an important societal task, particularly in view of future climate change. For this task, climate risk assessment increasingly uses model chains, which mainly build on data from the last few decades. The past record of events could play a role in this context. New numerical techniques can make use of historical weather data to simulate impacts quantitatively. However, using historical data for model applications differs from using recent products. Here, we provide an overview of climate risk assessment methodologies and of the properties of historical instrumental and documentary data. Using three examples, we then outline how historical environmental data can be used today in climate risk assessment by (1) developing and validating numerical model chains, (2) providing a large statistical sample which can be directly exploited to estimate hazards and to model present risks, and (3) establishing "worst-case" events which are relevant references in the present or future. The examples show that, in order to be successful, different sources (reanalyses, digitized instrumental data, and documentary data) and methods (dynamical downscaling and analog methods) need to be combined on a case-by-case basis.

The North China Plain (NCP) is one of the major areas of cereal production, and in recent years its maize (Zea mays L.) production has been influenced by both heat and chilling stresses. Adjusting the sowing date is an effective measure for mitigating these stresses. However, the underlying mechanisms remain poorly understood. We performed a 5‐year field experiment to determine how the sowing date mitigated heat and chilling stresses at Wuqiao Experimental Station in the NCP with three treatments: early sowing (ES), middle sowing (MS), and late sowing (LS). In all 5 years, higher grain yields were observed in the MS (averaged 11.7 Mg/ha) and LS (averaged 11.4Mg/ha) treatments compared with the ES (averaged 10.9Mg/ha) treatment. The lower yield in ES treatment mainly resulted from high temperature 5 days pre‐silking and 5 days post‐silking (>31.8°C). In 2015 and 2016, the lower grain yield in LS (11.4Mg/ha in 2015 and 11.2Mg/ha in 2016) treatment compared with MS (12.1Mg/ha in 2015 and 11.9Mg/ha in 2016) was mainly because the minimum temperature was

In this article we discuss several software packages that are designed to display and archive data from a personal weather station.

Precipitation and temperature time series suffer from many problems, such as short time, inadequate spatial coverage, missing data, and biases from various causes, which are particularly critical in remote areas such as Northern Canada. The development of alternative datasets for using as proxies for inadequate/missing weather data represents a key research area. In this paper, the performance of 6 alternative datasets is evaluated for hydrological modelling over 12 watersheds located across Canada and the contiguous United States. The datasets can be classified into 3 distinct categories: (a) interpolated gridded data, (b) reanalysis data, and (c) climate model outputs. Hydrological simulations were carried out using a lumped conceptual hydrological model calibrated using standard weather data and compared against results using a calibration specific to each alternative dataset. Prior to the hydrological simulations, the alternative datasets were all evaluated with respect to their ability to reproduce gridded daily precipitation and temperature characteristics over North America. The results show that both the reanalysis data and climate model data adequately represent the spatial pattern of daily precipitation and temperature over North America. The North American Regional Reanalysis (NARR) dataset consistently shows the best performance. With respect to hydrological modelling, the observed discharges are accurately represented by both the gridded and NARR datasets, and more so for the NARR data. The National Centers for Environmental Prediction dataset consistently performs worst as it is unable to even capture the seasonal pattern of observed streamflow for 3 out of the 12 watersheds. These results indicate that the NARR dataset could be used as a proxy for gauged precipitation and temperature for hydrological modelling over watersheds where observational datasets are deficient. The results also illustrate the ability of climate model data to be used for performing hydrological modelling when driven by reanalysis data at their boundaries, and especially so for high-resolution models.

Needle blight disease, caused by Passalora sequoiae, results in a progressive loss of leaf tissue on Leyland cypress (×Cupressocyparis leylandii) within container and field tree nurseries, in the landscape and on Christmas tree farms. Fungicide schedules have been developed in response to seasonal symptom progression and conidia dispersion without clarification of the influence of weather conditions on disease. Conidia count data of P. sequoiae were collected approximately weekly from June to December in 2001 in Watkinsville, GA, and in 2002 in Dearing, GA. Peak conidia numbers were trapped in September and October. Daily weather data summaries were obtained from a weather station at each location. Deterministic models were developed individually for summer (1 June to 30 September) and fall (1 October to 21 December) seasons. A three-day moving average (MA) of average daily temperature (avgT) had a positive quadratic relationship with conidia counts in the summer season. A 10-day MA of avgT, vapour pressure deficit (VPD) and total solar radiation (SolR) had a positive quadratic, a negative linear and positive linear relationship, respectively, with conidia counts in the fall season. The models show that the seasonal shift to cooler fall temperatures, along with lower VPD and SolR, favour production and dispersal of P. sequoiae conidia.

Decay of palsas can indicate permafrost status, particularly in regions where air temperatures have increased rapidly in recent decades. Using weather data, annual surveys of active-layer thickness, and analyses of high-resolution aerial imagery from the eastern Selwyn/western Mackenzie Mountains, NT, Canada, we show that permafrost temperatures have increased, active layers have deepened, and palsa areal extents have decreased considerably since the 1940s. High-altitude palsas thawed quickly from the 1940s to the 1980s, although some low-altitude palsas have recently decreased rapidly in areal extent due to peat-block calving. The linear rate of increasing active-layer thickness may not be congruent with the non-linear rate of areal loss of palsas. The rapid and episodic collapse of palsas at some sites highlights the necessity to consider hydrology, vegetation cover, landscape position, and morphology in palsa dynamics in addition to a warming climate. Copyright © 2017 John Wiley & Sons, Ltd.

Trend analysis of precipitation and drought will play a significant role in the future development and sustainable management of water resources. It is the main purpose of this paper to analyse meteorological droughts and trends. The variability of the standardized precipitation index (SPI) was investigated at 1, 3, 6, 9, 12 and 24 monthly intervals. The trends of 1, 3, 6, 9, 12 and 24 monthly SPI results were analysed by applying linear regression and the Mann–Kendall and Spearman's rho tests at the 5% significance level. The linear slopes of the trends were calculated with a technique proposed by Sen. Meteorological data from eight meteorological stations in the Aegean region of Turkey were used for the period 1960–2013. The monthly precipitation trend decreases in December, January, February and March in all regions according to the linear regression analysis results. Annual precipitation decreased at five stations. In drought analysis by the SPI, in a short time period (such as 3 months) drought is more frequent but shorter, and as the period increases the duration of drought also increases but frequency decreases. Winter droughts are beginning to occur in the class of ‘severe’ in recent years. In this study carried out for different time periods according to SPI values in all stations, the highest ranges are in the normal and mild drought degrees. In addition, severe and extreme droughts are also seen intensely frequently. Trend analysis of precipitation and drought is compared. The trend analyses of precipitation are in parallel with the results of drought analyses.

Many spatial vegetative physiological and environmental impact studies demand consistent as well as fine resolution daily meteorological data, which is often unavailable. This problem is evident when studies and applications focus at the regional and local scale. Meteorological stations cannot provide the needed data due to their irregular locations and several associated shortcomings such as missing data. Climate data from global sources like the National Centers for Environmental Prediction (NCEP) and WorldClim are provided at a coarse resolution which makes them inappropriate to use directly at regional and local scales. In order to circumvent this problem, we downscaled daily WorldClim and NCEP global climate grids to produce weather data sets using a delta method to 0.0083‾ (approximately 1 × 1 km) spatial resolution for the Amhara region of northwestern Ethiopia. The downscaled data set includes daily precipitation, and minimum and maximum temperature from 1979 to 2010. The drizzle effect in the downscaled data set was first eliminated by using a rain threshold that removed values of

An important part of agricultural adaptation is the timing of crop sowing dates, affecting yields and the level of risk incurred during a particular season. Cold stress is especially relevant in maize, Zea mays L., so that the timing of planting in the spring is a tactical response to short-term weather, but is also subject to strategic planning with regard to longer-term climate. Both factors compare the potential implications of cold stress to the additional yield obtainable through earlier planting. New cultivars suited to growing conditions in Europe and generally increasing spring temperatures have enabled earlier planting, but it is still dependent on short-term weather during the planting period. In the context of field-level decision-making, a panel regression is used to estimate the relationship between weekly local temperature and precipitation and planting dates at specific sites throughout Germany. Next, localised weather data and planting behaviour are linked to yields at the district (Landkreis) level to show the effects of planting date on yield. Based on these relationships optimal planting dates are explored with some associated costs and benefits. Results show a trend towards earlier planting that follows observed increasing spring temperatures and the availability of more cold-tolerant cultivars but this advance is buffered by the increasing severity of minimum temperatures during a critical period. Earlier planting potentially increases yield but this is offset by additional management costs and risk. A robust and simple depiction of farmer behaviour in climatic, technological and economic context can help to understand trends in crop management and productivity that effect agricultural landscapes.

The unique ability of wheat (Triticum aestivum L.) flour to produce cohesive dough has helped to make it the most widely used cereal crop for bread and other baked food products. Measurement of end-use qualities, such as loaf volume, is ideally carried out through assessing loaves of bread; however, this is resource intensive. Predictive testing methods are more often utilized to identify wheat genotypes with potentially acceptable loaf volume, although more accurate predictive methods would be beneficial. Our objectives were to study the influence of weather observations on bread loaf volume and flour and dough quality data, to use a neural network (NN) model to predict loaf volume with select input data, and to compare the best multiple regression models identified with their NN counterparts. Weather data collected at 20 days after heading (DAH) showed the highest correlations with bread loaf volume when compared with prior intervals. A NN model containing maximum, minimum, and nighttime temperatures pr...

Synoptic climatology has a long history of research where weather data are aggregated and composited to gain a better understanding of atmospheric effects on non-atmospheric variables. This has resulted in an applied scientific discipline that yields methods and tools designed for applications across disciplinary boundaries. The spatial synoptic classification (SSC) is an example of such a tool that helps researcher bridge methodological gaps between disciplines, especially those studying weather effects on human health. The SSC has been applied in several multi-discipline projects, and it appears that there is ample opportunity for growth into new topical areas. Likewise, there is opportunity for the SSC network to be expanded across the globe, especially into mid-latitude locations in the southern hemisphere. There is some question of the utility of the SSC in tropical locations, but such decisions must be based on the actual weather data from individual locations. Despite all of the strengths and potential uses of the SSC, there are some research problems, some locations, and some datasets for which it is not suitable. Nevertheless, the success of the SSC as a cross-disciplinary method is noteworthy because it has become a catalyst for collaboration.

This paper examines to what extent individual measures of well-being are correlated with daily weather patterns in the United Kingdom. Merging daily weather data with data from the British Household Panel Survey (BHPS) allows us to test whether measures of well-being are correlated with temperature, sunshine, rainfall and wind speed. We are able to make a strong case for causality due to ‘randomness’ of weather in addition to using regression methods that eliminate time-invariant individual level heterogeneity. Results suggest that some weather parameters (such as sunshine) are correlated with some measures of well-being (job satisfaction); however, in general the effect of weather on subjective measures of well-being is very small.

Hot summer days may lead to reduced thermal discomfort, labour productivity, and higher morbidity and mortality for vulnerable groups. The projected climate change may raise this thermal discomfort in the future. To implement measures to prevent adverse health conditions, robust estimates of the future human thermal comfort (HTC) are required. This study analyses the future HTC for both coastal and inland Dutch cities and countryside. The future conditions are based on the KNMI-06 climate scenarios. Using these scenarios, observed weather data from 1976 to 2005 are transformed to future weather design data representative for 2050. Subsequently, HTC expressed in the physiological equivalent temperature (PET) is estimated for these future scenarios. A substantial increase of heat stress abundance is foreseen in all climate scenarios, for both urban and rural areas, particularly, under the most intense warming. In these scenarios, the frequency of hours with heat stress will more than double, and the increase will develop faster in an urban canyon than in rural areas. In urban areas, PET shows a maximum as function of sky-view factor (SVF), i.e. for a smaller SVF a wind speed reduction increases the PET on one hand and shading reduces the PET on the other hand.

Detecting changes in the spatio-temporal patterns of temperature and precipitation is a prerequisite for developing effective adaptation options and strategies for the future. An effective method for assessing climate change and for providing information to decision makers and stakeholders is needed to implement appropriate adaptation strategies. The objective of this study was to determine whether climate change has caused spatio-temporal changes in meteorological elements in North Korea. We delineated the spatio-temporal patterns of temperature and precipitation caused by climate change in specific time periods based on statistically significant differences using a statistically robust method. Historical weather data from 27 meteorological stations over a 30-year period (1981–2010) were used. The results demonstrated that statistically significant changes occurred over the 30 years. The temporal trends in the maximum and minimum temperatures were highly significantly different in the western agricultural regions and central/southwest urban regions during 1996–2010 compared with 1981–1995. The precipitation amounts were significantly different in the southeast regions (around the coast). The numbers of precipitation events were significantly different for portions of the northern and northeast areas near the mountains. Additionally, statistically significant differences in the spatial structures of the temperature and precipitation were found at different time scales. The significant differences were not uniform in each season/month. Therefore, significant differences occurred in the meteorological elements, and particular locations and urbanized areas were affected by global warming. However, the temporal trends and spatial structures of each meteorological element were not equally modified; the meteorological changes occurred locally as a result of the changing climate.

SUMMARY On March 11, 2011, the power system suffered heavy damage from the huge earthquake and tsunami in the Tohoku region. This paper describes the change of features included in the time series of open demand data for the disaster of 3/11/2011. The demand data are separated into three sections: one for the “predisaster” analysis from April 2008 to February 2011, the second for the “during-disaster” survey from March to May 2011, and the last for the “postdisaster” analysis from June 2011 to March 2013. We distinguished the periodic components of daily, weekly, and seasonal fluctuation among the demand time series by applying a newly developed procedure, the quasi-ideal moving average method. Some significant changes were observed in the statistics of the predisaster and postdisaster periodic component analysis. The change in temperature sensitivity was also surveyed by using open weather data for Sendai.

Air temperature and sunshine duration for agricultural fields were extracted from 1-km-resolution weather data, and their relationship with winter wheat yield was compared for the adjacent Tokachi and Okhotsk regions of eastern Hokkaido, Japan. Major climatic drivers of winter wheat yield were air temperature during spring and grain-filling period in Tokachi, and sunshine duration during grain-filling period in Okhotsk. The regional difference in the sensitivity of yield to air temperature is attributed to variations in sunshine duration during grain-filling period. In Tokachi, a shorter grain-filling period limits the cumulative sunlight available to plants, because sunshine duration declines from late June to late July. In Okhotsk, yields of winter wheat were attributed to receiving adequate sunlight, although the growth period was curtailed by higher air temperature. We found that different climatic drivers affected winter wheat yield even in adjacent regions. The sensitivity of winter wheat yield to temperature can be determined by a threshold of 4.5 h daily sunshine duration regardless of region. Short sunshine duration during the grain-filling period exacerbated the sensitivity of wheat yield to temperature. Early summer in Okhotsk is characterized by sunnier weather than in Tokachi due to foehn, with respect to the southerly winds. Then, southerly wind was associated with significantly longer sunshine duration in Okhotsk. Regional cloud development affects sunshine duration as the climatic driver of wheat yield.

The Diebold–Mariano test for predictive accuracy has been used widely and adapted for economic forecasts, but has not seen much activity in weather forecast verification. The technique is applied to both simulated verification sets as well as weather data at eight stations in Utah, and a loss function based on dynamic time warping (DTW) is used. Results of the simulation experiment show that the DTW technique can be useful if timing errors are the concern. Real test cases demonstrate the difficulty in automating some of the more advanced methods proposed here, but also show the utility in even the most basic test, which is an improvement over similar tests that do not account for temporal and/or contemporaneous correlation.

Burn severity as inferred from satellite-derived differenced Normalized Burn Ratio (dNBR) is useful for evaluating fire impacts on ecosystems but the environmental controls on burn severity across large forest fires are both poorly understood and likely to be different than those influencing fire extent. We related dNBR to environmental variables including vegetation, topography, fire danger indices, and daily weather for daily areas burned on 42 large forest fires in central Idaho and western Montana. The 353 fire days we analyzed burned 111,200 ha as part of large fires in 2005, 2006, 2007, and 2011. We expected that local “bottom-up” variables like topography and vegetation would influence burn severity, but that our use of daily dNBR and weather data would uncover stronger relationships between the two than previous studies have shown. We found that percent existing vegetation cover had the largest influence on burn severity, while weather variables like fine fuel moisture, relative humidity, and wind...

Published in Agron. J. 106:227–235 (2014) doi:10.2134/agronj2013.0388 Available freely online through the author-supported open access option. Copyright © 2014 by the American Society of Agronomy, 5585 Guilford Road, Madison, WI 53711. All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. ABSTRACT Wheat (Triticum aestivum L.) yields in Kansas have increased due to wheat breeding and improved agronomic practices, but are subject to climate and disease challenges. e objective of this research is to quantify the impact of weather, disease, and genetic improvement on wheat yields of varieties grown in 11 locations in Kansas from 1985 to 2011. Wheat variety yield data from Kansas performance tests were matched with comprehensive location-speci c disease and weather data, including seasonal precipitation, monthly air temperature, air temperature and solar radiation around anthesis, and vapor pressure de cit (VPD). e results show that wheat breeding programs increased yield by 34 kg ha–1 yr–1. From 1985 through 2011, wheat breeding increased average wheat yields by 917 kg ha–1, or 27% of total yield. Weather was found to have a large impact on wheat yields. Simulations demonstrated that a 1°C increase in projected mean temperature was associated with a decrease in wheat yields of 715 kg ha–1, or 21%. Weather, diseases, and genetics all had signi cant impacts on wheat yields in 11 locations in Kansas during 1985 to 2011.

A chance observation of a drought-related plant mortality event in early 2014 in a normally wet and cool alpine area was matched with local weather data providing a unique insight into this event. The observed plant death was largely indiscriminate in areas that were topographically predisposed to being susceptible to drought. The weather conditions surrounding this event included 5 weeks with very little rain, an extreme heatwave and subsequent brief periods where warm temperatures and dry air combined to produce highly evaporative conditions. Extreme weather conditions such as this are expected to occur with increasing frequency as a result of climate change. Observing and reporting on real-world examples of how extreme weather events affect native vegetation is integral to improved climate change risk assessment and to inform future management actions.

Obtaining representative meteorological data for watershed-scale hydrological modelling can be difficult and time consuming. Land-based weather stations do not always adequately represent the weather occurring over a watershed, because they can be far from the watershed of interest and can have gaps in their data series, or recent data are not available. This study presents a method for using the Climate Forecast System Reanalysis (CFSR) global meteorological dataset to obtain historical weather data and demonstrates the application to modelling five watersheds representing different hydroclimate regimes. CFSR data are available globally for each hour since 1979 at a 38-km resolution. Results show that utilizing the CFSR precipitation and temperature data to force a watershed model provides stream discharge simulations that are as good as or better than models forced using traditional weather gauging stations, especially when stations are more than 10 km from the watershed. These results further demonstrate that adding CFSR data to the suite of watershed modelling tools provides new opportunities for meeting the challenges of modelling un-gauged watersheds and advancing real-time hydrological modelling. Copyright © 2013 John Wiley & Sons, Ltd.

Crop simulation models can be used to estimate impact of current and future climates on crop yields and food security, but require long-term historical daily weather data to obtain robust simulations. In many regions where crops are grown, daily weather data are not available. Alternatively, gridded weather databases (GWD) with complete terrestrial coverage are available, typically derived from: (i) global circulation computer models; (ii) interpolated weather station data; or (iii) remotely sensed surface data from satellites. The present study's objective is to evaluate capacity of GWDs to simulate crop yield potential (Yp) or water-limited yield potential (Yw), which can serve as benchmarks to assess impact of climate change scenarios on crop productivity and land use change. Three GWDs (CRU, NCEP/DOE, and NASA POWER data) were evaluated for their ability to simulate Yp and Yw of rice in China, USA maize, and wheat in Germany. Simulations of Yp and Yw based on recorded daily data from well-maintained weather stations were taken as the control weather data (CWD). Agreement between simulations of Yp or Yw based on CWD and those based on GWD was poor with the latter having strong bias and large root mean square errors (RMSEs) that were 26–72% of absolute mean yield across locations and years. In contrast, simulated Yp or Yw using observed daily weather data from stations in the NOAA database combined with solar radiation from the NASA-POWER database were in much better agreement with Yp and Yw simulated with CWD (i.e. little bias and an RMSE of 12–19% of the absolute mean). We conclude that results from studies that rely on GWD to simulate agricultural productivity in current and future climates are highly uncertain. An alternative approach would impose a climate scenario on location-specific observed daily weather databases combined with an appropriate upscaling method.

The aim of this study was to evaluate the performance of two predictive models for deoxynivalenol contamination of wheat at harvest in the Netherlands, including the use of weather forecast data and external model validation. Data were collected in a different year and from different wheat fields than data used for model development. The two models were run for six preset scenarios, varying in the period for which weather forecast data were used, from zero-day (historical data only) to a 13-day period around wheat flowering. Model predictions using forecast weather data were compared to those using historical data. Furthermore, model predictions using historical weather data were evaluated against observed deoxynivalenol contamination of the wheat fields. Results showed that the use of weather forecast data rather than observed data only slightly influenced model predictions. The percent of correct model predictions, given a threshold of 1,250 μg/kg (legal limit in European Union), was about 95% for the two models. However, only three samples had a deoxynivalenol concentration above this threshold, and the models were not able to predict these samples correctly. It was concluded that two- week weather forecast data can reliable be used in descriptive models for deoxynivalenol contamination of wheat, resulting in more timely model predictions. The two models are able to predict lower deoxynivalenol contamination correctly, but model performance in situations with high deoxynivalenol contamination needs to be further validated. This will need years with conducive environmental conditions for deoxynivalenol contamination of wheat.

Historical meteorological data are essential for increasing the level of understanding about past, present, and future climates. In the Northern Hemisphere, a significant amount of research has been dedicated to rescuing climate data from historical sources such as ship logbooks (e.g. RECLAIM, CLIWOC, and ICOADS). However, limited research in this field has focused on New Zealand, Southeast Australia, and the Southwest Pacific. Because these regions were colonized recently by Europeans (~200 years ago), only 50–100 years of land-based meteorological data exist for many locations. However, meteorological information contained in ship logbooks may extend and reinforce the existing historical climate record for these regions. The Log of Logs is a catalogue of ships that visited Australia, New Zealand, and surrounding waters in the th and 20th centuries of the Common Era. These volumes provide a record of the location of ship logbooks. This study extracted information from the Log of Logs for New Zealand, Southeast Australia, and the Southwest Pacific for 1786–1900. The purpose of this was to locate ship logbooks that may contain meteorological data. The next stage of this project is to gather, image, digitize, and to analyse the data from the prioritized logbooks. These data have application for local climate reconstruction, extension of regional circulation indices, and augmentation of the extended reanalysis without radiosondes effort.

In many hydro-meteorological applications, it is not always possible to get access to in situ weather measurements, especially for the ungauged catchments. This study explores the performances of downscaled weather data for Reference Evapotranspiration (ETo) retrieval using the global European Centre for Medium Range Weather Forecasts (ECMWF) ERA interim and National Centers for Environmental Prediction (NCEP) reanalysis data, simulated through Weather Research and Forecasting (WRF) mesoscale model. The range of the Nash-Sutcliffe efficiency calculated for the ECMWF pooled datasets derived ETo varies from 0.31 to 0.87, while for NCEP it is found to be 0.11 to 0.38. Bias and Root Mean Square Error (RMSE) are also indicating a very high discrepancy in the NCEP ETo (Bias = −0.05; RMSE = 0.11) as compared to ECMWF (Bias = 0.00; RMSE = 0.06). The overall findings reveal that ECMWF downscaled products have a much better performance than the NCEP's counterparts. Copyright © 2013 Royal Meteorological Society

Plant response to drought is complex, so that traits adapted to a specific drought type can confer disadvantage in another drought type. Understanding which type(s) of drought to target is of prime importance for crop improvement. Modelling was used to quantify seasonal drought patterns for a check variety across the Australian wheatbelt, using 123 yr of weather data for representative locations and managements. Two other genotypes were used to simulate the impact of maturity on drought pattern. Four major environment types summarized the variability in drought pattern over time and space. Severe stress beginning before flowering was common (44% of occurrences), with (24%) or without (20%) relief during grain filling. High variability occurred from year to year, differing with geographical region. With few exceptions, all four environment types occurred in most seasons, for each location, management system and genotype. Applications of such environment characterization are proposed to assist breeding and research to focus on germplasm, traits and genes of interest for target environments. The method was applied at a continental scale to highly variable environments and could be extended to other crops, to other drought-prone regions around the world, and to quantify potential changes in drought patterns under future climates.

Members of the public have for centuries recorded their observations of the natural world, including plant and animal distribution and phenology, water quality, weather data, and astronomical phenomena. Given the relatively recent growth of ecological research as a professional field of study, the historical contributions of amateurs to ecology can be easily overlooked. To better understand long-term changes in ecosystems, researchers are now revisiting many of these historical datasets collected by non-professionals. Over the past 100 years, scientific organizations have increasingly included volunteers in large-scale monitoring projects to broaden the geographical extent and sample size of observations. We believe that a renewed interest in citizen science, enriched with the perspective and data provided by the long tradition of public participation in science, will broaden the engagement of the public in ecological research and lead to new scientific insights.

Summary 1. Determining how events interact across stages of the annual cycle is critical for understanding the factors that affect individual fitness. However, there is currently little information detailing how breeding events influence migratory behaviour. 2. Using an automated digital telemetry array and an isolated island-breeding population of Savannah sparrows Passerculus sandwichensis, we provide the first direct evidence that the timing of breeding events carries over to influence the timing of migration in a songbird and assess for the first time how weather conditions on the breeding grounds also affect departure dates. 3. Date of migratory departure between September and October was strongly influenced by date of breeding completion in adults and fledging date in juveniles from June to July. 4. With respect to weather, adults departed during the first half of high-pressure systems, while juveniles departed throughout the entirety of high-pressure systems (including rainy evenings on the western edge of systems). 5. By combining both ecological and weather data, we could explain almost all variation in departure date for adults (95%), but weather conditions were not a good predictor of departure date for juveniles. 6. Our results provide strong evidence that the timing of breeding events is an important driver of migration timing and that exact departure dates are fine-tuned according to local weather conditions in adults, but not in juveniles.

Landscape-scale ecological modelling has been hindered by suitable high-resolution surface meteorological datasets. To overcome these limitations, desirable spatial attributes of gridded climate data are combined with desirable temporal attributes of regional-scale reanalysis and daily gauge-based precipitation to derive a spatially and temporally complete, high-resolution (4-km) gridded dataset of surface meteorological variables required in ecological modelling for the contiguous United States from 1979 to 2010. Validation of the resulting gridded surface meteorological data, using an extensive network of automated weather stations across the western United States, showed skill comparable to that derived from interpolation using station observations, suggesting it can serve as suitable surrogate for landscape-scale ecological modelling across vast unmonitored areas of the United States. Copyright © 2011 Royal Meteorological Society